Abstract
This paper describes the systems submitted by GadjahMada team to the Native Language Identification (NLI) Shared Task 2017. Our models used a continuous representation of character n-grams which are learned jointly with feed-forward neural network classifier. Character n-grams have been proved to be effective for stylebased identification tasks including NLI. Results on the test set demonstrate that the proposed model performs very well on essay and fusion tracks by obtaining more than 0.8 on both F-macro score and accuracy.
Cite
CITATION STYLE
Sari, Y., Fatchurrahman, M. R., & Dwiastuti, M. (2017). A shallow neural network for native language identification with character n-grams. In EMNLP 2017 - 12th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2017 - Proceedings of the Workshop (pp. 249–254). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-5027
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